Adaptive Synopsis of Non-Human Primates' Surveillance Video Based on Behavior Classification

Non-human primates NHPs play a critical role in biomedical research. Automated monitoring and analysis of NHP's behaviors through the surveillance video can greatly support the NHP-related studies. However, little research work has been undertaken yet. There are two challenges in analyzing the NHP's surveillance video: the NHP's behaviors are lack of regularity and intention, and serious occlusions are brought by the fences of the cages. In this paper, four typical NHPs' behaviors are defined based on the requirement in pharmaceutical analysis. We design a novel feature set combining contextual attributes and local motion information to overcome the effects of occlusions. A hierarchical linear discriminant analysis LDA classifier is proposed to categorize the NHPs' behaviors. Based on the behavior classification, an adaptive synopsis algorithm is further proposed to condense the NHPs' surveillance video, which offers a mechanism to retrieve any NHP's behavior information corresponding to specified events or time periods in the surveillance video. Experimental results show the effectiveness of the proposed method in categorizing and condensing NHPs' surveillance video.

[1]  Yanxi Liu,et al.  Image De-fencing Revisited , 2010, ACCV.

[3]  Kristen Grauman,et al.  Story-Driven Summarization for Egocentric Video , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  S. Blinder,et al.  Dynamic imaging on the high resolution research tomograph (HRRT): non-human primate studies , 2005, IEEE Nuclear Science Symposium Conference Record, 2005.

[5]  Yanxi Liu,et al.  Image de-fencing , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Jan-Olof Eklundh,et al.  Detecting Symmetry and Symmetric Constellations of Features , 2006, ECCV.

[7]  Gang Hua,et al.  A Hierarchical Visual Model for Video Object Summarization , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Kun Duan,et al.  Discovering localized attributes for fine-grained recognition , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Matthew S. Goodwin,et al.  Wearable wireless sensor platform for studying autonomic activity and social behavior in non-human primates , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[10]  David Salesin,et al.  Schematic storyboarding for video visualization and editing , 2006, SIGGRAPH 2006.

[11]  Irfan A. Essa,et al.  Augmenting Bag-of-Words: Data-Driven Discovery of Temporal and Structural Information for Activity Recognition , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Chong-Wah Ngo,et al.  Automatic video summarization by graph modeling , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[13]  Selcuk Sandikci,et al.  HMM Based behavior recognition of laboratory animals , 2012, ICPR 2012.

[14]  Patrick Lambert,et al.  Video summarization from spatio-temporal features , 2008, TVS '08.

[15]  C. Pudda,et al.  Epileptic seizure recordings of a non-human primate using carbon nanotube microelectrodes on implantable silicon shanks , 2011, 2011 5th International IEEE/EMBS Conference on Neural Engineering.